Makuhari, Chiba, Japan
September 26-30. 2010

Using a DBN to Integrate Sparse Classification and GMM-Based ASR

Yang Sun, Jort F. Gemmeke, Bert Cranen, Louis ten Bosch, Lou Boves

Radboud Universiteit Nijmegen, The Netherlands

The performance of an HMM-based speech recognizer using MFCCs as input is known to degrade
dramatically in noisy conditions. Recently, an exemplar-based noise robust ASR approach,
called sparse classification (SC), was introduced. While very successfully at lower SNRs,
the performance at high SNRs suffered when compared to HMM-based systems. In this work, we
propose to use a Dynamic Bayesian Network (DBN) to implement an HMM-model that uses both
MFCCs and phone predictions extracted from the SC system as input. By doing experiments on
the AURORA-2 connected digit recognition task, we show that our approach successfully
combines the strengths of both systems, resulting in competitive recognition accuracies at
both high and low SNRs.